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Due to lengthy sentences, crossdomain technical terms, and complex structure of patent claims, it is extremely difficult to extract open triples with traditional methods of Natural Language Processing (NLP) parsers. In this paper, we propose an Open Relation Extraction (ORE) approach with transforming relation extraction problem into sequence labeling problem in patent claims, which extract none predefined relationship triples from patent claims with a hybrid neural network architecture based on multihead attention mechanism. The hybrid neural network framework combined with Bi\u2010LSTM and CNN is proposed to extract argument phrase features and relation phrase features simultaneously. The Bi\u2010LSTM network gains long distance dependency features, and the CNN obtains local content feature; then, multihead attention mechanism is applied to get potential dependency relationship for time series of RNN model; the result of neural network proposed above applied to our constructed open patent relation dataset shows that our method outperforms both traditional classification algorithms of machine learning and the\u2010state\u2010of\u2010art neural network classification models in the measures of Precision, Recall, and F1.<\/jats:p>","DOI":"10.1155\/2021\/5547281","type":"journal-article","created":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T18:26:02Z","timestamp":1619634362000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Open Relation Extraction in Patent Claims with a Hybrid Network"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4253-6562","authenticated-orcid":false,"given":"Boting","family":"Geng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,4,28]]},"reference":[{"key":"e_1_2_7_1_2","doi-asserted-by":"crossref","unstructured":"ChenL. 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